629 lines
669 KiB
Plaintext
629 lines
669 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a3901bba-d66d-4358-89a7-50dc4b3dd91e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a4713d33-c5a2-4f49-8aed-873069543bec",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead tr th {\n",
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" text-align: left;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>Unnamed: 0_level_0</th>\n",
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" <th colspan=\"2\" halign=\"left\">原料特征</th>\n",
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" <th colspan=\"2\" halign=\"left\">热处理条件</th>\n",
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" <th colspan=\"3\" halign=\"left\">第一次热处理</th>\n",
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" <th colspan=\"2\" halign=\"left\">第二次热处理</th>\n",
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" <th>...</th>\n",
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" <th>模板剂</th>\n",
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" <th colspan=\"2\" halign=\"left\">活化剂</th>\n",
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" <th>混合方式</th>\n",
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" <th colspan=\"5\" halign=\"left\">碳材料结构特征</th>\n",
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" <th>参考文献</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>原料</th>\n",
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" <th>是否预处理</th>\n",
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" <th>软化点</th>\n",
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" <th>热处理次数</th>\n",
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" <th>是否是中温停留</th>\n",
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" <th>温度</th>\n",
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" <th>升温速率</th>\n",
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" <th>保留时间</th>\n",
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" <th>温度</th>\n",
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" <th>升温速率·</th>\n",
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" <th>...</th>\n",
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" <th>与沥青比例</th>\n",
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" <th>是否KOH活化</th>\n",
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" <th>比例</th>\n",
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" <th>混合方式</th>\n",
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" <th>比表面积</th>\n",
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" <th>总孔体积</th>\n",
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" <th>微孔体积</th>\n",
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" <th>平均孔径</th>\n",
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" <th>平均孔径.1</th>\n",
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" <th>Unnamed: 25_level_1</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>煤沥青</td>\n",
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" <td>否</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>否</td>\n",
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" <td>500</td>\n",
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" <td>5</td>\n",
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" <td>2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>...</td>\n",
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" <td>1.0</td>\n",
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" <td>是</td>\n",
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" <td>1.0</td>\n",
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" <td>溶剂</td>\n",
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" <td>908.07</td>\n",
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" <td>0.40</td>\n",
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" <td>0.34</td>\n",
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" <td>1.75</td>\n",
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" <td>NaN</td>\n",
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" <td>姜宇晨 硬模板法制备煤焦油沥青基多孔碳及其超电性能研究</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>煤沥青</td>\n",
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" <td>否</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>否</td>\n",
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" <td>600</td>\n",
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" <td>5</td>\n",
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" <td>2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>...</td>\n",
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" <td>1.0</td>\n",
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" <td>是</td>\n",
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" <td>0.5</td>\n",
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" <td>溶剂</td>\n",
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" <td>953.95</td>\n",
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" <td>0.66</td>\n",
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" <td>0.35</td>\n",
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" <td>2.76</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>煤沥青</td>\n",
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" <td>否</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>否</td>\n",
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" <td>600</td>\n",
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" <td>5</td>\n",
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" <td>2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>...</td>\n",
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" <td>1.0</td>\n",
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" <td>是</td>\n",
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" <td>1.0</td>\n",
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" <td>溶剂</td>\n",
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" <td>1388.62</td>\n",
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" <td>0.61</td>\n",
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" <td>0.53</td>\n",
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" <td>1.77</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>煤沥青</td>\n",
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" <td>否</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>否</td>\n",
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" <td>600</td>\n",
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" <td>5</td>\n",
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" <td>2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>...</td>\n",
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" <td>1.0</td>\n",
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" <td>是</td>\n",
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" <td>2.0</td>\n",
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" <td>溶剂</td>\n",
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" <td>1444.63</td>\n",
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|
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" <td>0.59</td>\n",
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|
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" <td>0.55</td>\n",
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|
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" <td>1.62</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>煤沥青</td>\n",
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" <td>否</td>\n",
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" <td>NaN</td>\n",
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" <td>2</td>\n",
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" <td>否</td>\n",
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" <td>500</td>\n",
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" <td>5</td>\n",
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" <td>2</td>\n",
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" <td>600.0</td>\n",
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" <td>5.0</td>\n",
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" <td>...</td>\n",
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|||
|
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" <td>1.0</td>\n",
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" <td>是</td>\n",
|
|||
|
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" <td>1.0</td>\n",
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|||
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" <td>溶剂</td>\n",
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|
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" <td>1020.99</td>\n",
|
|||
|
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" <td>0.45</td>\n",
|
|||
|
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" <td>0.35</td>\n",
|
|||
|
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" <td>1.77</td>\n",
|
|||
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" <td>NaN</td>\n",
|
|||
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 26 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Unnamed: 0_level_0 原料特征 热处理条件 第一次热处理 第二次热处理 \\\n",
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" 原料 是否预处理 软化点 热处理次数 是否是中温停留 温度 升温速率 保留时间 温度 升温速率· \n",
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"0 煤沥青 否 NaN 1 否 500 5 2 NaN NaN \n",
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"1 煤沥青 否 NaN 1 否 600 5 2 NaN NaN \n",
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"2 煤沥青 否 NaN 1 否 600 5 2 NaN NaN \n",
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"3 煤沥青 否 NaN 1 否 600 5 2 NaN NaN \n",
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"4 煤沥青 否 NaN 2 否 500 5 2 600.0 5.0 \n",
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"\n",
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" ... 模板剂 活化剂 混合方式 碳材料结构特征 \\\n",
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" ... 与沥青比例 是否KOH活化 比例 混合方式 比表面积 总孔体积 微孔体积 平均孔径 平均孔径.1 \n",
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"0 ... 1.0 是 1.0 溶剂 908.07 0.40 0.34 1.75 NaN \n",
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"1 ... 1.0 是 0.5 溶剂 953.95 0.66 0.35 2.76 NaN \n",
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"2 ... 1.0 是 1.0 溶剂 1388.62 0.61 0.53 1.77 NaN \n",
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"3 ... 1.0 是 2.0 溶剂 1444.63 0.59 0.55 1.62 NaN \n",
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"4 ... 1.0 是 1.0 溶剂 1020.99 0.45 0.35 1.77 NaN \n",
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"\n",
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" 参考文献 \n",
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|
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" Unnamed: 25_level_1 \n",
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"0 姜宇晨 硬模板法制备煤焦油沥青基多孔碳及其超电性能研究 \n",
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"1 NaN \n",
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"2 NaN \n",
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"3 NaN \n",
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"4 NaN \n",
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"\n",
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"[5 rows x 26 columns]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data = pd.read_excel('./data/20240102/20231227.xlsx', header=[0, 1])\n",
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"data.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8b113a99-8c56-455c-9ee8-3ac32a686e24",
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"metadata": {},
|
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"outputs": [],
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"source": [
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"data.columns = ['-'.join([y for y in x if 'Unnamed' not in y]) for x in data.columns]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "e134c799-b11c-46c8-898d-ac1d9e47e527",
|
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"metadata": {},
|
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"outputs": [],
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"source": [
|
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"ignore_cols = data.columns[:3].tolist() + data.columns[-2:].tolist()\n",
|
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"data.drop(columns=ignore_cols, inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "359c9cc6-2694-46a6-9f18-6361e220542a",
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"metadata": {},
|
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"outputs": [
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{
|
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"data": {
|
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"text/plain": [
|
|||
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"Index(['热处理条件-热处理次数', '热处理条件-是否是中温停留', '第一次热处理-温度', '第一次热处理-升温速率',\n",
|
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|
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" '第一次热处理-保留时间', '第二次热处理-温度', '第二次热处理-升温速率·', '第二次热处理-保留时间',\n",
|
|||
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" '共碳化-是否是共碳化物质', '共碳化-共碳化物质/沥青', '模板剂-与沥青比例', '活化剂-是否KOH活化', '活化剂-比例',\n",
|
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" '混合方式-混合方式', '碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积',\n",
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" '碳材料结构特征-平均孔径', '共碳化-种类_2-甲基咪唑', '共碳化-种类_三聚氰胺', '共碳化-种类_尿素',\n",
|
|||
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" '共碳化-种类_硫酸铵', '共碳化-种类_聚磷酸铵', '模板剂-模板剂制备方式_无', '模板剂-模板剂制备方式_溶液合成',\n",
|
|||
|
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" '模板剂-模板剂制备方式_热分解', '模板剂-模板剂制备方式_直接购买', '模板剂-模板剂制备方式_自己合成',\n",
|
|||
|
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" '模板剂-种类_Al2O3', '模板剂-种类_TiO2', '模板剂-种类_α-Fe2O3', '模板剂-种类_γ-Fe2O3',\n",
|
|||
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" '模板剂-种类_二氧化硅', '模板剂-种类_氢氧化镁', '模板剂-种类_氧化钙', '模板剂-种类_氧化锌', '模板剂-种类_氧化镁',\n",
|
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|
|
" '模板剂-种类_氯化钠', '模板剂-种类_氯化钾', '模板剂-种类_碱式碳酸镁', '模板剂-种类_碳酸钙', '模板剂-种类_纤维素'],\n",
|
|||
|
|
" dtype='object')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data.columns"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"id": "6cc22ecb-1070-4e63-a496-efc838094958",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.preprocessing import LabelEncoder\n",
|
|||
|
|
"encoder = LabelEncoder()\n",
|
|||
|
|
"for col in ['热处理条件-热处理次数', '热处理条件-是否是中温停留', '共碳化-是否是共碳化物质', '活化剂-是否KOH活化', '混合方式-混合方式']:\n",
|
|||
|
|
" encoded_labels = encoder.fit_transform(data[col])\n",
|
|||
|
|
" data[col] = encoded_labels"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"id": "24f58281-9f13-49ef-b44d-81d0644d6976",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"object_cols = ['共碳化-种类', '模板剂-模板剂制备方式', '模板剂-种类']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"id": "3368163e-85a1-4487-8078-be51cb5fb560",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data = pd.get_dummies(data, columns=object_cols)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"out_cols = [x for x in data.columns if '碳材料结构特征' in x]\n",
|
|||
|
|
"feature_cols = [x for x in data.columns if x not in out_cols]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"id": "e4946bd7-ae94-4981-82ed-66e2b496e035",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"train_data = data.reset_index(drop=True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"id": "4109685a-4d5b-4c63-b4e2-eb9db3989d02",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"import xgboost as xgb\n",
|
|||
|
|
"from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.model_selection import KFold, train_test_split\n",
|
|||
|
|
"kf = KFold(n_splits=10, shuffle=True, random_state=42)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"id": "42597842-1acb-4263-bdad-bfca7b11bcb5",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"import numpy as np"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"params_xgb = {\"objective\": 'reg:squarederror',\n",
|
|||
|
|
" \"subsample\": 0.9,\n",
|
|||
|
|
" \"max_depth\": 15,\n",
|
|||
|
|
" \"eta\": 0.05,\n",
|
|||
|
|
" \"gamma\": 0,\n",
|
|||
|
|
" \"lambda\": 1,\n",
|
|||
|
|
" \"alpha\": 0,\n",
|
|||
|
|
" \"colsample_bytree\": 0.8,}\n",
|
|||
|
|
"num_boost_round = 1000"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n",
|
|||
|
|
"plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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"<Figure size 1200x800 with 1 Axes>"
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]
|
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},
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|
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"metadata": {},
|
|||
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"output_type": "display_data"
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},
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{
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"data": {
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|||
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"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
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},
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{
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"eva_total = list()\n",
|
|||
|
|
"index_list = list()\n",
|
|||
|
|
"eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\n",
|
|||
|
|
"for col in out_cols:\n",
|
|||
|
|
" eva_list = list()\n",
|
|||
|
|
" train_data = train_data[~train_data[col].isna()].reset_index(drop=True)\n",
|
|||
|
|
" cur_test = list()\n",
|
|||
|
|
" cur_real = list()\n",
|
|||
|
|
" for (train_index, test_index) in kf.split(train_data):\n",
|
|||
|
|
" train = train_data.loc[train_index]\n",
|
|||
|
|
" valid = train_data.loc[test_index]\n",
|
|||
|
|
" X_train, Y_train = train[feature_cols], np.log1p(train[col])\n",
|
|||
|
|
" X_valid, Y_valid = valid[feature_cols], np.log1p(valid[col])\n",
|
|||
|
|
" dtrain = xgb.DMatrix(X_train, Y_train)\n",
|
|||
|
|
" dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
|
|||
|
|
" watchlist = [(dvalid, 'eval')]\n",
|
|||
|
|
" gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
|
|||
|
|
" early_stopping_rounds=50, verbose_eval=False)\n",
|
|||
|
|
" y_pred = np.expm1(gb_model.predict(xgb.DMatrix(X_valid)))\n",
|
|||
|
|
" y_true = np.expm1(Y_valid.values)\n",
|
|||
|
|
" MSE = mean_squared_error(y_true, y_pred)\n",
|
|||
|
|
" RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n",
|
|||
|
|
" MAE = mean_absolute_error(y_true, y_pred)\n",
|
|||
|
|
" MAPE = mean_absolute_percentage_error(y_true, y_pred)\n",
|
|||
|
|
" R_2 = r2_score(y_true, y_pred)\n",
|
|||
|
|
" cur_test.extend(y_pred[:7])\n",
|
|||
|
|
" cur_real.extend(y_true[:7])\n",
|
|||
|
|
" # print('MSE:', round(MSE, 4), end=', ')\n",
|
|||
|
|
" # print('RMSE:', round(RMSE, 4), end=', ')\n",
|
|||
|
|
" # print('MAE:', round(MAE, 4), end=', ')\n",
|
|||
|
|
" # print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n",
|
|||
|
|
" # print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n",
|
|||
|
|
" eva_list.append([MSE, RMSE, MAE, MAPE, R_2])\n",
|
|||
|
|
" plt.figure(figsize=(12, 8))\n",
|
|||
|
|
" plt.plot(range(len(cur_test)), cur_real, 'o-', label='real')\n",
|
|||
|
|
" plt.plot(range(len(cur_test)), cur_test, '*-', label='pred')\n",
|
|||
|
|
" plt.legend(loc='best')\n",
|
|||
|
|
" plt.title(f'{col}')\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
" eva_total.append(np.mean(eva_list, axis=0))\n",
|
|||
|
|
" index_list.append(f\"{col}\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"id": "dcce8331-256f-4e22-8ac5-f07dca12f3cf",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>MSE</th>\n",
|
|||
|
|
" <th>RMSE</th>\n",
|
|||
|
|
" <th>MAE</th>\n",
|
|||
|
|
" <th>MAPE</th>\n",
|
|||
|
|
" <th>R2</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>碳材料结构特征-比表面积</th>\n",
|
|||
|
|
" <td>303184.717382</td>\n",
|
|||
|
|
" <td>544.657385</td>\n",
|
|||
|
|
" <td>421.657610</td>\n",
|
|||
|
|
" <td>0.847088</td>\n",
|
|||
|
|
" <td>0.372696</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>碳材料结构特征-总孔体积</th>\n",
|
|||
|
|
" <td>0.128721</td>\n",
|
|||
|
|
" <td>0.354206</td>\n",
|
|||
|
|
" <td>0.280979</td>\n",
|
|||
|
|
" <td>0.900388</td>\n",
|
|||
|
|
" <td>0.435381</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>碳材料结构特征-微孔体积</th>\n",
|
|||
|
|
" <td>0.042627</td>\n",
|
|||
|
|
" <td>0.205114</td>\n",
|
|||
|
|
" <td>0.160595</td>\n",
|
|||
|
|
" <td>3.522258</td>\n",
|
|||
|
|
" <td>0.497659</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>碳材料结构特征-平均孔径</th>\n",
|
|||
|
|
" <td>0.675843</td>\n",
|
|||
|
|
" <td>0.763879</td>\n",
|
|||
|
|
" <td>0.509585</td>\n",
|
|||
|
|
" <td>0.172561</td>\n",
|
|||
|
|
" <td>0.307433</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" MSE RMSE MAE MAPE R2\n",
|
|||
|
|
"碳材料结构特征-比表面积 303184.717382 544.657385 421.657610 0.847088 0.372696\n",
|
|||
|
|
"碳材料结构特征-总孔体积 0.128721 0.354206 0.280979 0.900388 0.435381\n",
|
|||
|
|
"碳材料结构特征-微孔体积 0.042627 0.205114 0.160595 3.522258 0.497659\n",
|
|||
|
|
"碳材料结构特征-平均孔径 0.675843 0.763879 0.509585 0.172561 0.307433"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"pd.DataFrame.from_records(eva_total, index=index_list, columns=eva_cols)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "7ea3c810-754a-484b-b14e-e4fe571386b8",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3 (ipykernel)",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.8.16"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|